{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "676c5b74-68b9-4bc8-871c-749f5963ef6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入包\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b68db0ff-582a-4a9d-bf89-10b9e1a74b4b",
   "metadata": {},
   "source": [
    "## 统计图\n",
    "\n",
    "### catplot()函数\n",
    "\n",
    "#### 重点\n",
    "\n",
    "1. catplot主要用于绘制分类数据图，内部封装了barplot（默认）、pointplot和countplot。\n",
    "    1. barplot() ==> catplot(kind=\"bar\")\n",
    "    2. pointplot() ==> catplot(kind=\"point\")\n",
    "    3. countplot() ==> catplot(kind=\"count\")\n",
    "2. barplot图会自动进行统计（平均数、比例等），也可以使用estimator参数来修改统计函数\n",
    "3. pointplot图用绘制指定数据项取值的统计变化关系\n",
    "4. countplot图是用于统计指定数据项中每种取值的个数\n",
    "    * 注意：只能给x和y中的一项传值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3064ef63-579f-45e6-bfa3-e9e883a2d7e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据(seaborn提供测试数据)\n",
    "titanic = pd.read_csv(\"dataset/titanic.csv\")\n",
    "# 查看数据\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa45a57e-c4c7-422b-9e43-8eb0341b8ecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11182e0d0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制按“sex”取值分类的“survivied”分类统计图\n",
    "    # 注意：如果使用catplot绘制bar图需要加上 kind=\"bar\"\n",
    "    # 每个bar顶部的黑色线条代表95%的置信区间\n",
    "sns.catplot(x=\"sex\", y=\"survived\", data=titanic, kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f44f0394-b456-4c3b-b623-8550e62b29b1",
   "metadata": {},
   "source": [
    "#### estimator参数\n",
    "设定统计函数对y轴取值进行统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a311de85-9c95-465b-9e74-7c028c59006f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1938f6d50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 按sex取值分类统计总的survived值\n",
    "sns.catplot(x=\"sex\", y=\"survived\", data=titanic, kind=\"bar\", estimator=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0f97dae9-57a4-4149-ab7a-45716179c981",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1939c1150>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制pointplot图，分别统计sex取值下survived取值的变化情况\n",
    "sns.catplot(x=\"sex\", y=\"survived\", data=titanic, kind=\"point\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c5fcc7e-d587-4441-9592-8fc44d01020b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x193acb210>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 601x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制pointplot图，分别统计sex取值下survived取值的变化情况\n",
    "    # 使用hue参数进一步细分组别统计\n",
    "sns.catplot(x=\"sex\", y=\"survived\", data=titanic, kind=\"point\", hue=\"class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "07d2106f-9149-466e-be0d-71cca6dcfeb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x193b2ed10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制countplot图分别统计sex中取值为male和female的个数\n",
    "sns.catplot(x=\"sex\", data=titanic, kind=\"count\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
